In Python natural language processing, how do I parallelize workloads?

Parallel Processing, Natural Language Processing, Python, Workload Distribution
In Python, you can parallelize workloads in natural language processing using libraries like multiprocessing and joblib, which can significantly speed up data processing tasks by utilizing multiple CPU cores.
import multiprocessing from nltk.tokenize import word_tokenize # Function to process text def process_text(text): return word_tokenize(text) if __name__ == "__main__": texts = ["This is the first document.", "This is the second document.", "This is the third document."] # Create a pool of workers with multiprocessing.Pool(processes=4) as pool: results = pool.map(process_text, texts) print(results)

Parallel Processing Natural Language Processing Python Workload Distribution

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